• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집E
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• pp.392-397
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• 2001

A numerical study is performed to investigate the interaction between subsonic axial turbine blade boundary layer and periodically oncoming rotor induced wakes. An implicit scheme for solving the compressible Navier-Stokes equation is developed, which adopts a 4th-order compact difference for spatial discretiztion, a 2nd order Crank-Nicolson scheme for temporal discretization and the dynamic eddy viscosity model as the subgrid scale model. The efficiency and the accuracy of the proposed method are verified by applying to some benchmark problems such as laminar cylinder flow, laminar airfoil cascade flow and a transitional flat plate boundary layer flow. Computational results show good agreements with previous experimental and numerical results. Finally, flow through a stator cascade is simulated at $Re = 7.5{\times}10^5$ without free-stream turbulence intensity. The velocity fields and skin friction coefficients in the transitional region show similar trends with previous boundary layer natural transition.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집E
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• pp.386-391
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• 2001

A finite element code based on P2P1 tetra element has been developed for the large eddy simulation (LES) of turbulent flows around a complex geometry. Fractional 4-step algorithm is employed to obtain time accurate solution since it is less expensive than the integrated formulation, in which the velocity and pressure fields are solved at the same time. Crank-Nicolson method is used for second order temporal discretization and Galerkin method is adopted for spatial discretization. For very high Reynolds number flows, which would require a formidable number of nodes to resolve the flow field, SUPG (Streamline Upwind Petrov-Galerkin) method is applied to the quadratic interpolation function for velocity variables, Noting that the calculation of intrinsic time scale is very complicated when using SUPG for quadratic tetra element of velocity variables, the present study uses a unique intrinsic time scale proposed by Codina et al. since it makes the present three-dimensional unstructured code much simpler in terms of implementing SUPG. In order to see the effect of numerical diffusion caused by using an upwind scheme (SUPG), those obtained from P2P1 Galerkin method and P2P1 Petrov-Galerkin approach are compared for the flow around a sphere at some Reynolds number. Smagorinsky model is adopted as subgrid scale models in the context of P2P1 finite element method. As a benchmark problem for code validation, turbulent flows around a sphere and a MIRA model have been studied at various Reynolds numbers.

• 대한기계학회논문집B
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• 제22권7호
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• pp.1030-1040
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• 1998

One of the main unresolved issues in large-eddy simulation(LES) of wall-bounded turbulent flows is the requirement of high spatial resolution in the near-wall region, especially in the spanwise direction. Such high resolution required in the near-wall region is generally used throughout the computational domain, making simulations of high Reynolds number, complex-geometry flows prohibitive. A grid-embedding strategy using a nonconforming spectral domain-decomposition method is proposed to address this limitation. This method provides an efficient way of clustering grid points in the near-wall region with spectral accuracy. LES of transitional and turbulent channel flow has been performed to evaluate the proposed grid-embedding technique. The computational domain is divided into three subdomains to resolve the near-wall regions in the spanwise direction. Spectral patching collocation methods are used for the grid-embedding and appropriate conditions are suggested for the interface matching. Results of LES using the grid-embedding strategy are promising compared to LES of global spectral method and direct numerical simulation. Overall, the results show that the spectral domain-decomposition grid-embedding technique provides an efficient method for resolving the near-wall region in LES of complex flows of engineering interest, allowing significant savings in the computational CPU and memory.